Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance
Fig 5
Coarse-grained probability flux analysis (cgPFA).
cgPFA of (A) Levy flier to random walk simulations and (B) Random walk invariant simulations. cgPFA of (C) MycRas MEF, (D) WT MEF, (E) Myoblast FGF2+, and (F) MuSC FGF2+ motility states with subpaths of length τ = 20 time points (130 minutes). Each unique combination of bins between PC1 and PC2 is considered as a unique state. Arrows represent transition rate vectors, calculated for each state bin as the vector mean of transitions into the neighboring states in the von Neumann neighborhood. Arrow direction represents the direction of these transition rate vectors, and arrow length represents transition rate vector magnitude. Underlying colors represent the vector divergence from that state as a metric of state stability. Positive divergence indicates cells are more likely to leave a state, while negative divergence indicates cells are more likely to enter a state. 3D representations of (G) MEF WT, (H) Myoblast FGF2+, and (I) MuSC FGF2+ motility state divergence.